PART A
import os
import cv2
import keras
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pickle
from tensorflow.keras.applications.mobilenet import preprocess_input
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization
from tensorflow.keras.layers import UpSampling2D, Input, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras import backend
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential, Model
from sklearn.svm import SVC
from tqdm.notebook import tqdm
from zipfile import ZipFile
import tensorflow
import warnings
warnings.filterwarnings("ignore")
WARNING:tensorflow:From C:\Users\ruhee\anaconda3\anaconda\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
# Importing and reading 'images.npy'
data = np.load("C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/images (1).npy", allow_pickle = True)
data.shape
(409, 2)
data[0].shape
(2,)
data[0]
array([array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8),
list([{'label': ['Face'], 'notes': '', 'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063}, {'x': 0.1723076923076923, 'y': 0.45345345345345345}], 'imageWidth': 650, 'imageHeight': 333}, {'label': ['Face'], 'notes': '', 'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913}, {'x': 0.6584615384615384, 'y': 0.46846846846846846}], 'imageWidth': 650, 'imageHeight': 333}])],
dtype=object)
data[0][0].shape
(333, 650, 3)
data[0][0]
array([[[42, 37, 34],
[56, 51, 48],
[71, 66, 63],
...,
[23, 33, 34],
[26, 36, 37],
[28, 38, 39]],
[[40, 35, 32],
[51, 46, 43],
[64, 59, 56],
...,
[27, 36, 35],
[24, 33, 32],
[26, 35, 34]],
[[43, 38, 35],
[51, 46, 43],
[61, 56, 53],
...,
[28, 30, 27],
[33, 35, 32],
[35, 37, 34]],
...,
[[56, 47, 40],
[57, 48, 41],
[61, 52, 45],
...,
[67, 48, 42],
[55, 35, 28],
[60, 40, 33]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[60, 40, 33],
[54, 34, 27]],
[[53, 44, 37],
[54, 45, 38],
[57, 48, 41],
...,
[59, 40, 34],
[70, 50, 43],
[64, 44, 37]]], dtype=uint8)
data[0][1]
[{'label': ['Face'],
'notes': '',
'points': [{'x': 0.08615384615384615, 'y': 0.3063063063063063},
{'x': 0.1723076923076923, 'y': 0.45345345345345345}],
'imageWidth': 650,
'imageHeight': 333},
{'label': ['Face'],
'notes': '',
'points': [{'x': 0.583076923076923, 'y': 0.2912912912912913},
{'x': 0.6584615384615384, 'y': 0.46846846846846846}],
'imageWidth': 650,
'imageHeight': 333}]
# Checking the images in the dataset
fi,ax = plt.subplots(10,3,figsize = (20,30))
row = 0
col = 0
index = 0
for i in range(30):
ax[row][col].imshow(data[index][0], interpolation = 'nearest')
index = index + 12
col = col + 1
if col > 2:
row = row + 1
col = 0
plt.show()
# Creating features and labels
IMAGE_HEIGHT = 128
IMAGE_WIDTH = 128
HEIGHT_CELLS = 28
WIDTH_CELLS = 28
IMAGE_SIZE = 128
masks = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.zeros((int(data.shape[0]),IMAGE_HEIGHT, IMAGE_WIDTH, 3))
for index in range(data.shape[0]):
img = data[index][0]
img = cv2.resize(img, dsize = (IMAGE_HEIGHT, IMAGE_WIDTH), interpolation = cv2.INTER_CUBIC)
try:
img = img[:,:,:3]
except:
print(f"Exception {index} Grayscale image with shape {img.shape}")
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
continue
X[index] = preprocess_input(np.array(img, dtype=np.float32))
for i in data[index][1]:
x1 = int(i['points'][0]['x'] * IMAGE_WIDTH)
x2 = int(i['points'][1]['x'] * IMAGE_WIDTH)
y1 = int(i['points'][0]['y'] * IMAGE_HEIGHT)
y2 = int(i['points'][1]['y'] * IMAGE_HEIGHT)
masks[index][y1:y2, x1:x2] = 1
print(f"Shape of X is '{X.shape}' and the shape of mask is '{masks.shape}' ")
Exception 272 Grayscale image with shape (128, 128) Shape of X is '(409, 128, 128, 3)' and the shape of mask is '(409, 128, 128)'
# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, masks, test_size = 0.22)
X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size = 0.22)
print(f"Shape of X_train is '{X_train.shape}' and the shape of y_train is '{y_train.shape}'")
print(f"Shape of X_val is '{X_val.shape}' and the shape of y_val is '{y_val.shape}'")
print(f"Shape of X_test is '{X_test.shape}' and the shape of y_test is '{y_test.shape}'")
Shape of X_train is '(319, 128, 128, 3)' and the shape of y_train is '(319, 128, 128)' Shape of X_val is '(70, 128, 128, 3)' and the shape of y_val is '(70, 128, 128)' Shape of X_test is '(20, 128, 128, 3)' and the shape of y_test is '(20, 128, 128)'
# Selecting a random image from train image and displaying the original image
plt.imshow(X_train[2])
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<matplotlib.image.AxesImage at 0x2350e8dbbd0>
# Displaying the masked image
plt.imshow(y_train[2])
<matplotlib.image.AxesImage at 0x23517d0cb90>
# Creating a face mask detection model
IMAGE_SIZE = 128
ALPHA = 1
EPOCHS = 30
BATCH = 8
LR = 1e-4
model = MobileNet(input_shape = (IMAGE_SIZE, IMAGE_SIZE, 3), include_top = False, alpha = ALPHA, weights = "imagenet")
model.summary()
WARNING:tensorflow:From C:\Users\ruhee\anaconda3\anaconda\Lib\site-packages\keras\src\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.
WARNING:tensorflow:From C:\Users\ruhee\anaconda3\anaconda\Lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.
Model: "mobilenet_1.00_128"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 128, 128, 3)] 0
conv1 (Conv2D) (None, 64, 64, 32) 864
conv1_bn (BatchNormalizati (None, 64, 64, 32) 128
on)
conv1_relu (ReLU) (None, 64, 64, 32) 0
conv_dw_1 (DepthwiseConv2D (None, 64, 64, 32) 288
)
conv_dw_1_bn (BatchNormali (None, 64, 64, 32) 128
zation)
conv_dw_1_relu (ReLU) (None, 64, 64, 32) 0
conv_pw_1 (Conv2D) (None, 64, 64, 64) 2048
conv_pw_1_bn (BatchNormali (None, 64, 64, 64) 256
zation)
conv_pw_1_relu (ReLU) (None, 64, 64, 64) 0
conv_pad_2 (ZeroPadding2D) (None, 65, 65, 64) 0
conv_dw_2 (DepthwiseConv2D (None, 32, 32, 64) 576
)
conv_dw_2_bn (BatchNormali (None, 32, 32, 64) 256
zation)
conv_dw_2_relu (ReLU) (None, 32, 32, 64) 0
conv_pw_2 (Conv2D) (None, 32, 32, 128) 8192
conv_pw_2_bn (BatchNormali (None, 32, 32, 128) 512
zation)
conv_pw_2_relu (ReLU) (None, 32, 32, 128) 0
conv_dw_3 (DepthwiseConv2D (None, 32, 32, 128) 1152
)
conv_dw_3_bn (BatchNormali (None, 32, 32, 128) 512
zation)
conv_dw_3_relu (ReLU) (None, 32, 32, 128) 0
conv_pw_3 (Conv2D) (None, 32, 32, 128) 16384
conv_pw_3_bn (BatchNormali (None, 32, 32, 128) 512
zation)
conv_pw_3_relu (ReLU) (None, 32, 32, 128) 0
conv_pad_4 (ZeroPadding2D) (None, 33, 33, 128) 0
conv_dw_4 (DepthwiseConv2D (None, 16, 16, 128) 1152
)
conv_dw_4_bn (BatchNormali (None, 16, 16, 128) 512
zation)
conv_dw_4_relu (ReLU) (None, 16, 16, 128) 0
conv_pw_4 (Conv2D) (None, 16, 16, 256) 32768
conv_pw_4_bn (BatchNormali (None, 16, 16, 256) 1024
zation)
conv_pw_4_relu (ReLU) (None, 16, 16, 256) 0
conv_dw_5 (DepthwiseConv2D (None, 16, 16, 256) 2304
)
conv_dw_5_bn (BatchNormali (None, 16, 16, 256) 1024
zation)
conv_dw_5_relu (ReLU) (None, 16, 16, 256) 0
conv_pw_5 (Conv2D) (None, 16, 16, 256) 65536
conv_pw_5_bn (BatchNormali (None, 16, 16, 256) 1024
zation)
conv_pw_5_relu (ReLU) (None, 16, 16, 256) 0
conv_pad_6 (ZeroPadding2D) (None, 17, 17, 256) 0
conv_dw_6 (DepthwiseConv2D (None, 8, 8, 256) 2304
)
conv_dw_6_bn (BatchNormali (None, 8, 8, 256) 1024
zation)
conv_dw_6_relu (ReLU) (None, 8, 8, 256) 0
conv_pw_6 (Conv2D) (None, 8, 8, 512) 131072
conv_pw_6_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_pw_6_relu (ReLU) (None, 8, 8, 512) 0
conv_dw_7 (DepthwiseConv2D (None, 8, 8, 512) 4608
)
conv_dw_7_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_dw_7_relu (ReLU) (None, 8, 8, 512) 0
conv_pw_7 (Conv2D) (None, 8, 8, 512) 262144
conv_pw_7_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_pw_7_relu (ReLU) (None, 8, 8, 512) 0
conv_dw_8 (DepthwiseConv2D (None, 8, 8, 512) 4608
)
conv_dw_8_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_dw_8_relu (ReLU) (None, 8, 8, 512) 0
conv_pw_8 (Conv2D) (None, 8, 8, 512) 262144
conv_pw_8_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_pw_8_relu (ReLU) (None, 8, 8, 512) 0
conv_dw_9 (DepthwiseConv2D (None, 8, 8, 512) 4608
)
conv_dw_9_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_dw_9_relu (ReLU) (None, 8, 8, 512) 0
conv_pw_9 (Conv2D) (None, 8, 8, 512) 262144
conv_pw_9_bn (BatchNormali (None, 8, 8, 512) 2048
zation)
conv_pw_9_relu (ReLU) (None, 8, 8, 512) 0
conv_dw_10 (DepthwiseConv2 (None, 8, 8, 512) 4608
D)
conv_dw_10_bn (BatchNormal (None, 8, 8, 512) 2048
ization)
conv_dw_10_relu (ReLU) (None, 8, 8, 512) 0
conv_pw_10 (Conv2D) (None, 8, 8, 512) 262144
conv_pw_10_bn (BatchNormal (None, 8, 8, 512) 2048
ization)
conv_pw_10_relu (ReLU) (None, 8, 8, 512) 0
conv_dw_11 (DepthwiseConv2 (None, 8, 8, 512) 4608
D)
conv_dw_11_bn (BatchNormal (None, 8, 8, 512) 2048
ization)
conv_dw_11_relu (ReLU) (None, 8, 8, 512) 0
conv_pw_11 (Conv2D) (None, 8, 8, 512) 262144
conv_pw_11_bn (BatchNormal (None, 8, 8, 512) 2048
ization)
conv_pw_11_relu (ReLU) (None, 8, 8, 512) 0
conv_pad_12 (ZeroPadding2D (None, 9, 9, 512) 0
)
conv_dw_12 (DepthwiseConv2 (None, 4, 4, 512) 4608
D)
conv_dw_12_bn (BatchNormal (None, 4, 4, 512) 2048
ization)
conv_dw_12_relu (ReLU) (None, 4, 4, 512) 0
conv_pw_12 (Conv2D) (None, 4, 4, 1024) 524288
conv_pw_12_bn (BatchNormal (None, 4, 4, 1024) 4096
ization)
conv_pw_12_relu (ReLU) (None, 4, 4, 1024) 0
conv_dw_13 (DepthwiseConv2 (None, 4, 4, 1024) 9216
D)
conv_dw_13_bn (BatchNormal (None, 4, 4, 1024) 4096
ization)
conv_dw_13_relu (ReLU) (None, 4, 4, 1024) 0
conv_pw_13 (Conv2D) (None, 4, 4, 1024) 1048576
conv_pw_13_bn (BatchNormal (None, 4, 4, 1024) 4096
ization)
conv_pw_13_relu (ReLU) (None, 4, 4, 1024) 0
=================================================================
Total params: 3228864 (12.32 MB)
Trainable params: 3206976 (12.23 MB)
Non-trainable params: 21888 (85.50 KB)
_________________________________________________________________
def create_model(trainable = False):
model = MobileNet(input_shape = (IMAGE_SIZE, IMAGE_SIZE, 3), include_top = False, alpha = ALPHA, weights = "imagenet")
for layer in model.layers:
layer.trainable = trainable
block00 = model.layers[0].input
block01 = model.get_layer("conv_pw_1_relu").output
block02 = model.get_layer("conv_pw_2_relu").output
block03 = model.get_layer("conv_pw_3_relu").output
block05 = model.get_layer("conv_pw_5_relu").output
block11 = model.get_layer("conv_pw_11_relu").output
block13 = model.get_layer("conv_pw_13_relu").output
decoderBlock = Concatenate()([UpSampling2D()(block13), block11])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block05])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block03])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block01])
decoderBlock = Concatenate()([UpSampling2D()(decoderBlock), block00])
decoderBlock = Conv2D(1, kernel_size = 1, activation = "sigmoid")(decoderBlock)
decoderBlock = Reshape((IMAGE_SIZE, IMAGE_SIZE))(decoderBlock)
return Model(inputs = model.input, outputs = decoderBlock)
model = create_model(False)
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 128, 128, 3)] 0 []
conv1 (Conv2D) (None, 64, 64, 32) 864 ['input_2[0][0]']
conv1_bn (BatchNormalizati (None, 64, 64, 32) 128 ['conv1[0][0]']
on)
conv1_relu (ReLU) (None, 64, 64, 32) 0 ['conv1_bn[0][0]']
conv_dw_1 (DepthwiseConv2D (None, 64, 64, 32) 288 ['conv1_relu[0][0]']
)
conv_dw_1_bn (BatchNormali (None, 64, 64, 32) 128 ['conv_dw_1[0][0]']
zation)
conv_dw_1_relu (ReLU) (None, 64, 64, 32) 0 ['conv_dw_1_bn[0][0]']
conv_pw_1 (Conv2D) (None, 64, 64, 64) 2048 ['conv_dw_1_relu[0][0]']
conv_pw_1_bn (BatchNormali (None, 64, 64, 64) 256 ['conv_pw_1[0][0]']
zation)
conv_pw_1_relu (ReLU) (None, 64, 64, 64) 0 ['conv_pw_1_bn[0][0]']
conv_pad_2 (ZeroPadding2D) (None, 65, 65, 64) 0 ['conv_pw_1_relu[0][0]']
conv_dw_2 (DepthwiseConv2D (None, 32, 32, 64) 576 ['conv_pad_2[0][0]']
)
conv_dw_2_bn (BatchNormali (None, 32, 32, 64) 256 ['conv_dw_2[0][0]']
zation)
conv_dw_2_relu (ReLU) (None, 32, 32, 64) 0 ['conv_dw_2_bn[0][0]']
conv_pw_2 (Conv2D) (None, 32, 32, 128) 8192 ['conv_dw_2_relu[0][0]']
conv_pw_2_bn (BatchNormali (None, 32, 32, 128) 512 ['conv_pw_2[0][0]']
zation)
conv_pw_2_relu (ReLU) (None, 32, 32, 128) 0 ['conv_pw_2_bn[0][0]']
conv_dw_3 (DepthwiseConv2D (None, 32, 32, 128) 1152 ['conv_pw_2_relu[0][0]']
)
conv_dw_3_bn (BatchNormali (None, 32, 32, 128) 512 ['conv_dw_3[0][0]']
zation)
conv_dw_3_relu (ReLU) (None, 32, 32, 128) 0 ['conv_dw_3_bn[0][0]']
conv_pw_3 (Conv2D) (None, 32, 32, 128) 16384 ['conv_dw_3_relu[0][0]']
conv_pw_3_bn (BatchNormali (None, 32, 32, 128) 512 ['conv_pw_3[0][0]']
zation)
conv_pw_3_relu (ReLU) (None, 32, 32, 128) 0 ['conv_pw_3_bn[0][0]']
conv_pad_4 (ZeroPadding2D) (None, 33, 33, 128) 0 ['conv_pw_3_relu[0][0]']
conv_dw_4 (DepthwiseConv2D (None, 16, 16, 128) 1152 ['conv_pad_4[0][0]']
)
conv_dw_4_bn (BatchNormali (None, 16, 16, 128) 512 ['conv_dw_4[0][0]']
zation)
conv_dw_4_relu (ReLU) (None, 16, 16, 128) 0 ['conv_dw_4_bn[0][0]']
conv_pw_4 (Conv2D) (None, 16, 16, 256) 32768 ['conv_dw_4_relu[0][0]']
conv_pw_4_bn (BatchNormali (None, 16, 16, 256) 1024 ['conv_pw_4[0][0]']
zation)
conv_pw_4_relu (ReLU) (None, 16, 16, 256) 0 ['conv_pw_4_bn[0][0]']
conv_dw_5 (DepthwiseConv2D (None, 16, 16, 256) 2304 ['conv_pw_4_relu[0][0]']
)
conv_dw_5_bn (BatchNormali (None, 16, 16, 256) 1024 ['conv_dw_5[0][0]']
zation)
conv_dw_5_relu (ReLU) (None, 16, 16, 256) 0 ['conv_dw_5_bn[0][0]']
conv_pw_5 (Conv2D) (None, 16, 16, 256) 65536 ['conv_dw_5_relu[0][0]']
conv_pw_5_bn (BatchNormali (None, 16, 16, 256) 1024 ['conv_pw_5[0][0]']
zation)
conv_pw_5_relu (ReLU) (None, 16, 16, 256) 0 ['conv_pw_5_bn[0][0]']
conv_pad_6 (ZeroPadding2D) (None, 17, 17, 256) 0 ['conv_pw_5_relu[0][0]']
conv_dw_6 (DepthwiseConv2D (None, 8, 8, 256) 2304 ['conv_pad_6[0][0]']
)
conv_dw_6_bn (BatchNormali (None, 8, 8, 256) 1024 ['conv_dw_6[0][0]']
zation)
conv_dw_6_relu (ReLU) (None, 8, 8, 256) 0 ['conv_dw_6_bn[0][0]']
conv_pw_6 (Conv2D) (None, 8, 8, 512) 131072 ['conv_dw_6_relu[0][0]']
conv_pw_6_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_pw_6[0][0]']
zation)
conv_pw_6_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_6_bn[0][0]']
conv_dw_7 (DepthwiseConv2D (None, 8, 8, 512) 4608 ['conv_pw_6_relu[0][0]']
)
conv_dw_7_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_dw_7[0][0]']
zation)
conv_dw_7_relu (ReLU) (None, 8, 8, 512) 0 ['conv_dw_7_bn[0][0]']
conv_pw_7 (Conv2D) (None, 8, 8, 512) 262144 ['conv_dw_7_relu[0][0]']
conv_pw_7_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_pw_7[0][0]']
zation)
conv_pw_7_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_7_bn[0][0]']
conv_dw_8 (DepthwiseConv2D (None, 8, 8, 512) 4608 ['conv_pw_7_relu[0][0]']
)
conv_dw_8_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_dw_8[0][0]']
zation)
conv_dw_8_relu (ReLU) (None, 8, 8, 512) 0 ['conv_dw_8_bn[0][0]']
conv_pw_8 (Conv2D) (None, 8, 8, 512) 262144 ['conv_dw_8_relu[0][0]']
conv_pw_8_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_pw_8[0][0]']
zation)
conv_pw_8_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_8_bn[0][0]']
conv_dw_9 (DepthwiseConv2D (None, 8, 8, 512) 4608 ['conv_pw_8_relu[0][0]']
)
conv_dw_9_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_dw_9[0][0]']
zation)
conv_dw_9_relu (ReLU) (None, 8, 8, 512) 0 ['conv_dw_9_bn[0][0]']
conv_pw_9 (Conv2D) (None, 8, 8, 512) 262144 ['conv_dw_9_relu[0][0]']
conv_pw_9_bn (BatchNormali (None, 8, 8, 512) 2048 ['conv_pw_9[0][0]']
zation)
conv_pw_9_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_9_bn[0][0]']
conv_dw_10 (DepthwiseConv2 (None, 8, 8, 512) 4608 ['conv_pw_9_relu[0][0]']
D)
conv_dw_10_bn (BatchNormal (None, 8, 8, 512) 2048 ['conv_dw_10[0][0]']
ization)
conv_dw_10_relu (ReLU) (None, 8, 8, 512) 0 ['conv_dw_10_bn[0][0]']
conv_pw_10 (Conv2D) (None, 8, 8, 512) 262144 ['conv_dw_10_relu[0][0]']
conv_pw_10_bn (BatchNormal (None, 8, 8, 512) 2048 ['conv_pw_10[0][0]']
ization)
conv_pw_10_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_10_bn[0][0]']
conv_dw_11 (DepthwiseConv2 (None, 8, 8, 512) 4608 ['conv_pw_10_relu[0][0]']
D)
conv_dw_11_bn (BatchNormal (None, 8, 8, 512) 2048 ['conv_dw_11[0][0]']
ization)
conv_dw_11_relu (ReLU) (None, 8, 8, 512) 0 ['conv_dw_11_bn[0][0]']
conv_pw_11 (Conv2D) (None, 8, 8, 512) 262144 ['conv_dw_11_relu[0][0]']
conv_pw_11_bn (BatchNormal (None, 8, 8, 512) 2048 ['conv_pw_11[0][0]']
ization)
conv_pw_11_relu (ReLU) (None, 8, 8, 512) 0 ['conv_pw_11_bn[0][0]']
conv_pad_12 (ZeroPadding2D (None, 9, 9, 512) 0 ['conv_pw_11_relu[0][0]']
)
conv_dw_12 (DepthwiseConv2 (None, 4, 4, 512) 4608 ['conv_pad_12[0][0]']
D)
conv_dw_12_bn (BatchNormal (None, 4, 4, 512) 2048 ['conv_dw_12[0][0]']
ization)
conv_dw_12_relu (ReLU) (None, 4, 4, 512) 0 ['conv_dw_12_bn[0][0]']
conv_pw_12 (Conv2D) (None, 4, 4, 1024) 524288 ['conv_dw_12_relu[0][0]']
conv_pw_12_bn (BatchNormal (None, 4, 4, 1024) 4096 ['conv_pw_12[0][0]']
ization)
conv_pw_12_relu (ReLU) (None, 4, 4, 1024) 0 ['conv_pw_12_bn[0][0]']
conv_dw_13 (DepthwiseConv2 (None, 4, 4, 1024) 9216 ['conv_pw_12_relu[0][0]']
D)
conv_dw_13_bn (BatchNormal (None, 4, 4, 1024) 4096 ['conv_dw_13[0][0]']
ization)
conv_dw_13_relu (ReLU) (None, 4, 4, 1024) 0 ['conv_dw_13_bn[0][0]']
conv_pw_13 (Conv2D) (None, 4, 4, 1024) 1048576 ['conv_dw_13_relu[0][0]']
conv_pw_13_bn (BatchNormal (None, 4, 4, 1024) 4096 ['conv_pw_13[0][0]']
ization)
conv_pw_13_relu (ReLU) (None, 4, 4, 1024) 0 ['conv_pw_13_bn[0][0]']
up_sampling2d (UpSampling2 (None, 8, 8, 1024) 0 ['conv_pw_13_relu[0][0]']
D)
concatenate (Concatenate) (None, 8, 8, 1536) 0 ['up_sampling2d[0][0]',
'conv_pw_11_relu[0][0]']
up_sampling2d_1 (UpSamplin (None, 16, 16, 1536) 0 ['concatenate[0][0]']
g2D)
concatenate_1 (Concatenate (None, 16, 16, 1792) 0 ['up_sampling2d_1[0][0]',
) 'conv_pw_5_relu[0][0]']
up_sampling2d_2 (UpSamplin (None, 32, 32, 1792) 0 ['concatenate_1[0][0]']
g2D)
concatenate_2 (Concatenate (None, 32, 32, 1920) 0 ['up_sampling2d_2[0][0]',
) 'conv_pw_3_relu[0][0]']
up_sampling2d_3 (UpSamplin (None, 64, 64, 1920) 0 ['concatenate_2[0][0]']
g2D)
concatenate_3 (Concatenate (None, 64, 64, 1984) 0 ['up_sampling2d_3[0][0]',
) 'conv_pw_1_relu[0][0]']
up_sampling2d_4 (UpSamplin (None, 128, 128, 1984) 0 ['concatenate_3[0][0]']
g2D)
concatenate_4 (Concatenate (None, 128, 128, 1987) 0 ['up_sampling2d_4[0][0]',
) 'input_2[0][0]']
conv2d (Conv2D) (None, 128, 128, 1) 1988 ['concatenate_4[0][0]']
reshape (Reshape) (None, 128, 128) 0 ['conv2d[0][0]']
==================================================================================================
Total params: 3230852 (12.32 MB)
Trainable params: 1988 (7.77 KB)
Non-trainable params: 3228864 (12.32 MB)
__________________________________________________________________________________________________
# Defining dice coefficient
def dice_coefficient(y_true, y_pred):
numerator = 2 * tf.reduce_sum(y_true * y_pred)
denominator = tf.reduce_sum(y_true + y_pred)
return numerator / (denominator + tf.keras.backend.epsilon())
# Defining loss function
def loss(y_true, y_pred):
return binary_crossentropy(y_true, y_pred) - tf.keras.backend.log(dice_coefficient(y_true, y_pred) + tf.keras.backend.epsilon())
optimizer = optimizer = tf.keras.optimizers.Nadam(LR)
metrics = [dice_coefficient, Recall(), Precision()]
model.compile(loss = loss, optimizer = optimizer, metrics = metrics)
callbacks = [ReduceLROnPlateau(monitor = 'val_loss', factor = 0.1, patience = 4),
EarlyStopping(monitor = 'val_loss', patience = 10, restore_best_weights = False)]
train_steps = len(X_train)//BATCH
valid_steps = len(X_val)//BATCH
if len(X_train) % BATCH != 0:
train_steps += 1
if len(X_val) % BATCH != 0:
valid_steps += 1
model.fit(X_train, y_train, validation_data = (X_val, y_val), epochs = EPOCHS, steps_per_epoch = train_steps,
validation_steps = valid_steps, callbacks = callbacks)
Epoch 1/30 WARNING:tensorflow:From C:\Users\ruhee\anaconda3\anaconda\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead. WARNING:tensorflow:From C:\Users\ruhee\anaconda3\anaconda\Lib\site-packages\keras\src\engine\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead. 40/40 [==============================] - 48s 1s/step - loss: 2.0901 - dice_coefficient: 0.2267 - recall: 0.3654 - precision: 0.1904 - val_loss: 2.0184 - val_dice_coefficient: 0.2236 - val_recall: 0.3477 - val_precision: 0.2252 - lr: 1.0000e-04 Epoch 2/30 40/40 [==============================] - 35s 879ms/step - loss: 1.7497 - dice_coefficient: 0.2776 - recall: 0.3821 - precision: 0.3065 - val_loss: 1.7228 - val_dice_coefficient: 0.2760 - val_recall: 0.4107 - val_precision: 0.3084 - lr: 1.0000e-04 Epoch 3/30 40/40 [==============================] - 35s 870ms/step - loss: 1.5342 - dice_coefficient: 0.3253 - recall: 0.4469 - precision: 0.3970 - val_loss: 1.5193 - val_dice_coefficient: 0.3155 - val_recall: 0.4074 - val_precision: 0.3924 - lr: 1.0000e-04 Epoch 4/30 40/40 [==============================] - 34s 853ms/step - loss: 1.3393 - dice_coefficient: 0.3727 - recall: 0.4817 - precision: 0.4664 - val_loss: 1.3800 - val_dice_coefficient: 0.3497 - val_recall: 0.4244 - val_precision: 0.4500 - lr: 1.0000e-04 Epoch 5/30 40/40 [==============================] - 35s 866ms/step - loss: 1.2369 - dice_coefficient: 0.4037 - recall: 0.5053 - precision: 0.5153 - val_loss: 1.2815 - val_dice_coefficient: 0.3778 - val_recall: 0.4436 - val_precision: 0.4860 - lr: 1.0000e-04 Epoch 6/30 40/40 [==============================] - 36s 910ms/step - loss: 1.1484 - dice_coefficient: 0.4302 - recall: 0.5130 - precision: 0.5517 - val_loss: 1.2072 - val_dice_coefficient: 0.4015 - val_recall: 0.4689 - val_precision: 0.5101 - lr: 1.0000e-04 Epoch 7/30 40/40 [==============================] - 36s 900ms/step - loss: 1.1083 - dice_coefficient: 0.4457 - recall: 0.5314 - precision: 0.5719 - val_loss: 1.1484 - val_dice_coefficient: 0.4218 - val_recall: 0.4929 - val_precision: 0.5267 - lr: 1.0000e-04 Epoch 8/30 40/40 [==============================] - 36s 901ms/step - loss: 1.0709 - dice_coefficient: 0.4615 - recall: 0.5593 - precision: 0.5765 - val_loss: 1.1031 - val_dice_coefficient: 0.4375 - val_recall: 0.4992 - val_precision: 0.5453 - lr: 1.0000e-04 Epoch 9/30 40/40 [==============================] - 34s 852ms/step - loss: 1.0154 - dice_coefficient: 0.4785 - recall: 0.5534 - precision: 0.6005 - val_loss: 1.0720 - val_dice_coefficient: 0.4479 - val_recall: 0.4872 - val_precision: 0.5687 - lr: 1.0000e-04 Epoch 10/30 40/40 [==============================] - 34s 864ms/step - loss: 0.9792 - dice_coefficient: 0.4954 - recall: 0.5794 - precision: 0.5987 - val_loss: 1.0446 - val_dice_coefficient: 0.4584 - val_recall: 0.4904 - val_precision: 0.5829 - lr: 1.0000e-04 Epoch 11/30 40/40 [==============================] - 35s 868ms/step - loss: 0.9561 - dice_coefficient: 0.5025 - recall: 0.5712 - precision: 0.6163 - val_loss: 1.0192 - val_dice_coefficient: 0.4689 - val_recall: 0.5026 - val_precision: 0.5887 - lr: 1.0000e-04 Epoch 12/30 40/40 [==============================] - 33s 837ms/step - loss: 0.9325 - dice_coefficient: 0.5119 - recall: 0.5725 - precision: 0.6263 - val_loss: 1.0005 - val_dice_coefficient: 0.4764 - val_recall: 0.5013 - val_precision: 0.6009 - lr: 1.0000e-04 Epoch 13/30 40/40 [==============================] - 33s 832ms/step - loss: 0.9047 - dice_coefficient: 0.5241 - recall: 0.5930 - precision: 0.6234 - val_loss: 0.9817 - val_dice_coefficient: 0.4850 - val_recall: 0.5171 - val_precision: 0.6004 - lr: 1.0000e-04 Epoch 14/30 40/40 [==============================] - 34s 857ms/step - loss: 0.8887 - dice_coefficient: 0.5310 - recall: 0.5906 - precision: 0.6328 - val_loss: 0.9648 - val_dice_coefficient: 0.4929 - val_recall: 0.5349 - val_precision: 0.5988 - lr: 1.0000e-04 Epoch 15/30 40/40 [==============================] - 35s 870ms/step - loss: 0.8793 - dice_coefficient: 0.5364 - recall: 0.6051 - precision: 0.6302 - val_loss: 0.9515 - val_dice_coefficient: 0.4995 - val_recall: 0.5482 - val_precision: 0.5967 - lr: 1.0000e-04 Epoch 16/30 40/40 [==============================] - 34s 846ms/step - loss: 0.8752 - dice_coefficient: 0.5395 - recall: 0.6275 - precision: 0.6232 - val_loss: 0.9479 - val_dice_coefficient: 0.4998 - val_recall: 0.5118 - val_precision: 0.6240 - lr: 1.0000e-04 Epoch 17/30 40/40 [==============================] - 33s 834ms/step - loss: 0.8436 - dice_coefficient: 0.5509 - recall: 0.5938 - precision: 0.6502 - val_loss: 0.9310 - val_dice_coefficient: 0.5085 - val_recall: 0.5419 - val_precision: 0.6130 - lr: 1.0000e-04 Epoch 18/30 40/40 [==============================] - 34s 847ms/step - loss: 0.8348 - dice_coefficient: 0.5561 - recall: 0.6168 - precision: 0.6405 - val_loss: 0.9273 - val_dice_coefficient: 0.5099 - val_recall: 0.5276 - val_precision: 0.6244 - lr: 1.0000e-04 Epoch 19/30 40/40 [==============================] - 35s 878ms/step - loss: 0.8291 - dice_coefficient: 0.5582 - recall: 0.6024 - precision: 0.6563 - val_loss: 0.9162 - val_dice_coefficient: 0.5157 - val_recall: 0.5442 - val_precision: 0.6197 - lr: 1.0000e-04 Epoch 20/30 40/40 [==============================] - 36s 903ms/step - loss: 0.8183 - dice_coefficient: 0.5644 - recall: 0.6202 - precision: 0.6486 - val_loss: 0.9093 - val_dice_coefficient: 0.5191 - val_recall: 0.5469 - val_precision: 0.6216 - lr: 1.0000e-04 Epoch 21/30 40/40 [==============================] - 34s 863ms/step - loss: 0.8123 - dice_coefficient: 0.5661 - recall: 0.6204 - precision: 0.6537 - val_loss: 0.9067 - val_dice_coefficient: 0.5204 - val_recall: 0.5387 - val_precision: 0.6284 - lr: 1.0000e-04 Epoch 22/30 40/40 [==============================] - 34s 857ms/step - loss: 0.7939 - dice_coefficient: 0.5750 - recall: 0.6258 - precision: 0.6533 - val_loss: 0.9006 - val_dice_coefficient: 0.5237 - val_recall: 0.5441 - val_precision: 0.6279 - lr: 1.0000e-04 Epoch 23/30 40/40 [==============================] - 33s 836ms/step - loss: 0.7982 - dice_coefficient: 0.5724 - recall: 0.6215 - precision: 0.6601 - val_loss: 0.8937 - val_dice_coefficient: 0.5273 - val_recall: 0.5517 - val_precision: 0.6257 - lr: 1.0000e-04 Epoch 24/30 40/40 [==============================] - 34s 855ms/step - loss: 0.7951 - dice_coefficient: 0.5754 - recall: 0.6283 - precision: 0.6587 - val_loss: 0.8894 - val_dice_coefficient: 0.5296 - val_recall: 0.5513 - val_precision: 0.6286 - lr: 1.0000e-04 Epoch 25/30 40/40 [==============================] - 35s 870ms/step - loss: 0.7851 - dice_coefficient: 0.5803 - recall: 0.6349 - precision: 0.6581 - val_loss: 0.8877 - val_dice_coefficient: 0.5304 - val_recall: 0.5438 - val_precision: 0.6341 - lr: 1.0000e-04 Epoch 26/30 40/40 [==============================] - 35s 869ms/step - loss: 0.7769 - dice_coefficient: 0.5834 - recall: 0.6242 - precision: 0.6693 - val_loss: 0.8776 - val_dice_coefficient: 0.5365 - val_recall: 0.5743 - val_precision: 0.6187 - lr: 1.0000e-04 Epoch 27/30 40/40 [==============================] - 35s 875ms/step - loss: 0.7800 - dice_coefficient: 0.5836 - recall: 0.6426 - precision: 0.6588 - val_loss: 0.8801 - val_dice_coefficient: 0.5346 - val_recall: 0.5474 - val_precision: 0.6357 - lr: 1.0000e-04 Epoch 28/30 40/40 [==============================] - 34s 849ms/step - loss: 0.7909 - dice_coefficient: 0.5786 - recall: 0.6300 - precision: 0.6712 - val_loss: 0.8699 - val_dice_coefficient: 0.5408 - val_recall: 0.5792 - val_precision: 0.6179 - lr: 1.0000e-04 Epoch 29/30 40/40 [==============================] - 34s 850ms/step - loss: 0.7783 - dice_coefficient: 0.5848 - recall: 0.6424 - precision: 0.6651 - val_loss: 0.8690 - val_dice_coefficient: 0.5407 - val_recall: 0.5656 - val_precision: 0.6293 - lr: 1.0000e-04 Epoch 30/30 40/40 [==============================] - 34s 856ms/step - loss: 0.7752 - dice_coefficient: 0.5869 - recall: 0.6505 - precision: 0.6622 - val_loss: 0.8650 - val_dice_coefficient: 0.5431 - val_recall: 0.5719 - val_precision: 0.6260 - lr: 1.0000e-04
<keras.src.callbacks.History at 0x23515773ad0>
test_steps = (len(X_test)//BATCH)
if len(X_test) % BATCH != 0:
test_steps += 1
model.evaluate(X_test, y_test, steps = test_steps)
3/3 [==============================] - 2s 519ms/step - loss: 0.8766 - dice_coefficient: 0.5747 - recall: 0.6911 - precision: 0.5668
[0.8766239285469055, 0.5747478604316711, 0.6911271214485168, 0.5668086409568787]
n = 3
test_image = X_train[n]
print(test_image.shape)
plt.imshow(test_image)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
(128, 128, 3)
<matplotlib.image.AxesImage at 0x2351692a490>
test_mask = masks[n]
plt.imshow(test_mask)
<matplotlib.image.AxesImage at 0x2358fb60650>
test_image = np.resize(test_image, (1, 128, 128, 3))
pred_mask = model.predict(x = test_image)
pred_mask = np.resize(pred_mask, (128, 128))
plt.imshow(pred_mask)
1/1 [==============================] - 1s 827ms/step
<matplotlib.image.AxesImage at 0x2358fc0d3d0>
final_pred_mask = np.resize(pred_mask, (128, 128))
final_image = np.resize(test_image, (128, 128, 3))
plt.imshow(final_image)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
<matplotlib.image.AxesImage at 0x23597250650>
fig = plt.figure()
img_1 = plt.imshow(final_image)
img_2 = plt.imshow(final_pred_mask, alpha = 0.6)
plt.show()
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
PART B
import cv2
import glob
import os
import pandas as pd
from zipfile import ZipFile
# Extract images from the zip file
zip_file_path = "C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/training_images-20211126T092819Z-001.zip"
extract_path = "C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/Part B"
with ZipFile(zip_file_path, 'r') as zip:
zip.extractall(extract_path)
# Load the face cascade
cascPath = "haarcascade_frontalface_alt2.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
# Create a list to store face metadata
face_metadata_list = []
# Process each image in the extracted folder
image_folder = os.path.join(extract_path, "*.jpg")
for image_path in glob.glob(image_folder):
# Load the image
image = cv2.imread(image_path)
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30))
print("Found {0} faces in {1}".format(len(faces), image_path))
# Crop Padding
left = 10
right = 10
top = 10
bottom = 10
# Process each detected face
for (x, y, w, h) in faces:
print("Face coordinates:", x, y, w, h)
# Extract metadata
file_name = os.path.basename(image_path)
face_metadata = {
"Image": file_name,
"X": x,
"Y": y,
"Width": w,
"Height": h,
}
# Append metadata to the list
face_metadata_list.append(face_metadata)
# Save cropped image
cropped_image = image[y - top : y + h + bottom, x - left : x + w + right]
cropped_file_path = os.path.join(extract_path, "cropped_{0}_{1}.jpg".format(str(file_name), str(x)))
cv2.imwrite(cropped_file_path, cropped_image)
print("Saved:", cropped_file_path)
# Convert the list of face metadata to a DataFrame
df_faces = pd.DataFrame(face_metadata_list)
# Save the DataFrame to a CSV file
csv_file_path = "face_metadata.csv"
df_faces.to_csv(csv_file_path, index=False)
# Display the DataFrame
print("DataFrame saved to:", csv_file_path)
DataFrame saved to: face_metadata.csv
PART C
with ZipFile("C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/PINS.zip", 'r') as zip:
zip.extractall()
class IdentityMetadata():
def __init__(self, base, name, file):
# print(base, name, file)
# dataset base directory
self.base = base
# identity name
self.name = name
# image file name
self.file = file
def __repr__(self):
return self.image_path()
def image_path(self):
return os.path.join(self.base, self.name, self.file)
def load_metadata(path):
metadata = []
exts = []
for i in os.listdir(path):
for f in os.listdir(os.path.join(path, i)):
# Check file extension. Allow only jpg/jpeg' files.
ext = os.path.splitext(f)[1]
if ext == '.jpg' or ext == '.jpeg':
metadata.append(IdentityMetadata(path, i, f))
exts.append(ext)
return np.array(metadata), exts
metadata, exts = load_metadata('PINS')
labels = np.array([meta.name for meta in metadata])
def load_image(path):
img = cv2.imread(path, 1)
# OpenCV loads images with color channels
# in BGR order. So we need to reverse them
return img[...,::-1]
load_image(metadata[0].image_path())
array([[[233, 188, 165],
[231, 186, 163],
[220, 178, 156],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
[[232, 187, 164],
[233, 188, 165],
[220, 178, 156],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
[[230, 186, 161],
[228, 184, 159],
[206, 164, 140],
...,
[106, 63, 72],
[106, 63, 72],
[106, 63, 72]],
...,
[[198, 118, 91],
[197, 120, 94],
[195, 119, 95],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]],
[[200, 120, 93],
[200, 120, 93],
[198, 121, 95],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]],
[[201, 121, 94],
[201, 121, 94],
[199, 122, 96],
...,
[ 78, 66, 76],
[ 78, 66, 76],
[ 78, 66, 76]]], dtype=uint8)
def vgg_face():
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape = (224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation = 'relu'))
model.add(MaxPooling2D((2,2), strides = (2,2)))
model.add(Convolution2D(4096, (7, 7), activation = 'relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation = 'relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
return model
model = vgg_face()
model.load_weights("C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/vgg_face_weights.h5")
print(model.summary())
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d (ZeroPaddin (None, 226, 226, 3) 0
g2D)
conv2d_1 (Conv2D) (None, 224, 224, 64) 1792
zero_padding2d_1 (ZeroPadd (None, 226, 226, 64) 0
ing2D)
conv2d_2 (Conv2D) (None, 224, 224, 64) 36928
max_pooling2d (MaxPooling2 (None, 112, 112, 64) 0
D)
zero_padding2d_2 (ZeroPadd (None, 114, 114, 64) 0
ing2D)
conv2d_3 (Conv2D) (None, 112, 112, 128) 73856
zero_padding2d_3 (ZeroPadd (None, 114, 114, 128) 0
ing2D)
conv2d_4 (Conv2D) (None, 112, 112, 128) 147584
max_pooling2d_1 (MaxPoolin (None, 56, 56, 128) 0
g2D)
zero_padding2d_4 (ZeroPadd (None, 58, 58, 128) 0
ing2D)
conv2d_5 (Conv2D) (None, 56, 56, 256) 295168
zero_padding2d_5 (ZeroPadd (None, 58, 58, 256) 0
ing2D)
conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
zero_padding2d_6 (ZeroPadd (None, 58, 58, 256) 0
ing2D)
conv2d_7 (Conv2D) (None, 56, 56, 256) 590080
max_pooling2d_2 (MaxPoolin (None, 28, 28, 256) 0
g2D)
zero_padding2d_7 (ZeroPadd (None, 30, 30, 256) 0
ing2D)
conv2d_8 (Conv2D) (None, 28, 28, 512) 1180160
zero_padding2d_8 (ZeroPadd (None, 30, 30, 512) 0
ing2D)
conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
zero_padding2d_9 (ZeroPadd (None, 30, 30, 512) 0
ing2D)
conv2d_10 (Conv2D) (None, 28, 28, 512) 2359808
max_pooling2d_3 (MaxPoolin (None, 14, 14, 512) 0
g2D)
zero_padding2d_10 (ZeroPad (None, 16, 16, 512) 0
ding2D)
conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_11 (ZeroPad (None, 16, 16, 512) 0
ding2D)
conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
zero_padding2d_12 (ZeroPad (None, 16, 16, 512) 0
ding2D)
conv2d_13 (Conv2D) (None, 14, 14, 512) 2359808
max_pooling2d_4 (MaxPoolin (None, 7, 7, 512) 0
g2D)
conv2d_14 (Conv2D) (None, 1, 1, 4096) 102764544
dropout (Dropout) (None, 1, 1, 4096) 0
conv2d_15 (Conv2D) (None, 1, 1, 4096) 16781312
dropout_1 (Dropout) (None, 1, 1, 4096) 0
conv2d_16 (Conv2D) (None, 1, 1, 2622) 10742334
flatten (Flatten) (None, 2622) 0
activation (Activation) (None, 2622) 0
=================================================================
Total params: 145002878 (553.14 MB)
Trainable params: 145002878 (553.14 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None
vgg_face_descriptor = Model(inputs = model.layers[0].input, outputs = model.layers[-2].output)
# Get embedding vector for first image in the metadata using the pre-trained model
img_path = metadata[0].image_path()
img = load_image(img_path)
# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)
img = cv2.resize(img, dsize = (224, 224))
print(img.shape)
# Obtain embedding vector for an image
# Get the embedding vector for the above image using vgg_face_descriptor model and print the shape
embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis = 0))[0]
print(embedding_vector.shape)
(224, 224, 3) 1/1 [==============================] - 1s 1s/step (2622,)
embeddings = []
embeddings = np.zeros((metadata.shape[0], 2622))
for i, meta in tqdm(enumerate(metadata)):
try:
image = load_image(str(meta))
image = (image/255.).astype(np.float32)
image = cv2.resize(image, (224, 224))
embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(image, axis = 0))[0]
except:
embeddings[i] = np.zeros(2622)
0it [00:00, ?it/s]
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1s 539ms/step 1/1 [==============================] - 1s 561ms/step 1/1 [==============================] - 1s 533ms/step 1/1 [==============================] - 1s 544ms/step 1/1 [==============================] - 1s 546ms/step 1/1 [==============================] - 1s 518ms/step 1/1 [==============================] - 1s 532ms/step 1/1 [==============================] - 1s 514ms/step 1/1 [==============================] - 1s 531ms/step 1/1 [==============================] - 0s 484ms/step 1/1 [==============================] - 1s 519ms/step 1/1 [==============================] - 0s 460ms/step 1/1 [==============================] - 1s 505ms/step 1/1 [==============================] - 0s 462ms/step 1/1 [==============================] - 0s 494ms/step 1/1 [==============================] - 0s 491ms/step 1/1 [==============================] - 1s 502ms/step 1/1 [==============================] - 1s 517ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 1s 514ms/step 1/1 [==============================] - 1s 504ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 0s 495ms/step 1/1 [==============================] - 1s 516ms/step 1/1 [==============================] - 1s 518ms/step 1/1 [==============================] - 1s 528ms/step 1/1 [==============================] - 1s 547ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 0s 486ms/step 1/1 [==============================] - 1s 523ms/step 1/1 [==============================] - 1s 518ms/step 1/1 [==============================] - 1s 549ms/step 1/1 [==============================] - 1s 503ms/step 1/1 [==============================] - 1s 516ms/step 1/1 [==============================] - 1s 504ms/step 1/1 [==============================] - 1s 526ms/step 1/1 [==============================] - 1s 548ms/step 1/1 [==============================] - 0s 459ms/step 1/1 [==============================] - 0s 454ms/step 1/1 [==============================] - 0s 460ms/step 1/1 [==============================] - 0s 467ms/step 1/1 [==============================] - 0s 464ms/step 1/1 [==============================] - 1s 517ms/step 1/1 [==============================] - 0s 489ms/step 1/1 [==============================] - 1s 514ms/step 1/1 [==============================] - 0s 490ms/step 1/1 [==============================] - 0s 488ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 1s 505ms/step 1/1 [==============================] - 0s 493ms/step 1/1 [==============================] - 1s 504ms/step 1/1 [==============================] - 1s 502ms/step 1/1 [==============================] - 0s 493ms/step 1/1 [==============================] - 0s 491ms/step 1/1 [==============================] - 1s 501ms/step 1/1 [==============================] - 0s 489ms/step 1/1 [==============================] - 1s 516ms/step 1/1 [==============================] - 0s 500ms/step 1/1 [==============================] - 0s 497ms/step 1/1 [==============================] - 0s 497ms/step 1/1 [==============================] - 0s 498ms/step 1/1 [==============================] - 0s 499ms/step 1/1 [==============================] - 1s 534ms/step 1/1 [==============================] - 1s 517ms/step 1/1 [==============================] - 0s 495ms/step 1/1 [==============================] - 0s 495ms/step 1/1 [==============================] - 1s 506ms/step 1/1 [==============================] - 0s 493ms/step 1/1 [==============================] - 0s 493ms/step 1/1 [==============================] - 0s 442ms/step 1/1 [==============================] - 0s 497ms/step 1/1 [==============================] - 1s 500ms/step
def distance(emb1, emb2):
return np.sum(np.square(emb1 - emb2))
def show_pair(idx1, idx2):
plt.figure(figsize=(8,3))
plt.suptitle(f'Distance = {distance(embeddings[idx1], embeddings[idx2]):.2f}')
plt.subplot(121)
plt.imshow(load_image(metadata[idx1].image_path()))
plt.subplot(122)
plt.imshow(load_image(metadata[idx2].image_path()));
show_pair(2, 3)
show_pair(2, 180)
show_pair(30, 31)
show_pair(30, 100)
show_pair(70, 72)
show_pair(70, 115)
train_idx = np.arange(metadata.shape[0]) % 9 != 0
test_idx = np.arange(metadata.shape[0]) % 9 == 0
# one half as train examples of 10 identities
X_train = embeddings[train_idx]
# another half as test examples of 10 identities
X_test = embeddings[test_idx]
targets = np.array([m.name for m in metadata])
y_train = targets[train_idx]
y_test = targets[test_idx]
from sklearn.preprocessing import LabelEncoder
# Encode labels
en = LabelEncoder()
y_train = en.fit_transform(y_train)
y_test = en.transform(y_test)
from sklearn.preprocessing import StandardScaler
# Standarize features
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)
# Covariance matrix
cov_matrix = np.cov(X_train_sc.T)
# Eigen values and vector
eig_vals, eig_vecs = np.linalg.eig(cov_matrix)
# Cumulative variance explained
tot = sum(eig_vals)
var_exp = [(i /tot) * 100 for i in sorted(eig_vals, reverse = True)]
cum_var_exp = np.cumsum(var_exp)
print('Cumulative Variance Explained', cum_var_exp)
Cumulative Variance Explained [ 13.58416799 18.9486785 22.94242518 ... 99.99999983 99.99999999 100. ]
# Get index where cumulative variance explained is > threshold
thres = 95
res = list(filter(lambda i: i > thres, cum_var_exp))[0]
index = (cum_var_exp.tolist().index(res))
print(f'Index of element just greater than {thres}: {str(index)}')
Index of element just greater than 95: 346
# Ploting
plt.figure(figsize = (15 , 7.2))
plt.bar(range(1, eig_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')
plt.step(range(1, eig_vals.size + 1), cum_var_exp, where = 'mid', label = 'Cumulative explained variance')
plt.axhline(y = thres, color = 'r', linestyle = '--')
plt.axvline(x = index, color = 'r', linestyle = '--')
plt.ylabel('Explained Variance Ratio')
plt.xlabel('Principal Components')
plt.legend(loc = 'best')
plt.tight_layout()
plt.show()
# Reducing the dimensions
from sklearn.decomposition import PCA
pca = PCA(n_components = index, random_state = 50, svd_solver = 'full', whiten = True)
pca.fit(X_train_sc)
X_train_pca = pca.transform(X_train_sc)
X_test_pca = pca.transform(X_test_sc)
display(X_train_pca.shape, X_test_pca.shape)
(9573, 346)
(1197, 346)
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
params_grid = [{'kernel': ['rbf'], 'gamma': [1e-2, 1e-3, 1e-4], 'C': [1, 10, 100, 1000], 'class_weight': ['balanced', None]}]
svc = GridSearchCV(SVC(random_state = 50), params_grid, cv = 3, scoring = 'f1_macro', verbose = 50, n_jobs = -1)
svc.fit(X_train_pca, y_train)
print('Best estimator found by grid search:')
print(svc.best_estimator_)
# Best estimator found by grid search:
SVC(C = 1, gamma = 0.001, kernel = 'rbf', class_weight = 'balanced')
Fitting 3 folds for each of 24 candidates, totalling 72 fits Best estimator found by grid search: SVC(C=10, class_weight='balanced', gamma=0.0001, random_state=50)
SVC(C=1, class_weight='balanced', gamma=0.001)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(C=1, class_weight='balanced', gamma=0.001)
svc_pca = SVC(C = 1, gamma = 0.001, kernel = 'rbf', class_weight = 'balanced', random_state = 50)
svc_pca.fit(X_train_pca, y_train)
print('SVC accuracy for train set: {0:.3f}'.format(svc_pca.score(X_train_pca, y_train)))
SVC accuracy for train set: 0.994
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
# Predict
y_pred = svc_pca.predict(X_test_pca)
# Accuracy Score
print('Accuracy Score: {}'.format(accuracy_score(y_test, y_pred).round(3)))
Accuracy Score: 0.966
names = [name.split('_')[1].title().strip() for name in labels]
# Classification Report
print('Classification Report: \n{}'.format(classification_report(y_test, y_pred, target_names = np.unique(names))))
Classification Report:
precision recall f1-score support
Aaron Paul 1.00 1.00 1.00 10
Alexandra Daddario 0.91 1.00 0.95 10
Alvaro Morte 1.00 1.00 1.00 14
Alycia Debnam Carey Face 1.00 1.00 1.00 12
Amanda Crew 1.00 1.00 1.00 7
Amaury Nolasco 1.00 0.89 0.94 9
Amber Heard Face 1.00 1.00 1.00 7
Anna Gunn 1.00 1.00 1.00 14
Anne Hathaway 1.00 1.00 1.00 14
Barbara Palvin Face 1.00 1.00 1.00 9
Bellamy Blake Face 0.87 0.93 0.90 14
Benedict Cumberbatch 1.00 1.00 1.00 12
Betsy Brandt 1.00 1.00 1.00 10
Bill Gates 1.00 0.90 0.95 10
Brenton Thwaites 0.94 1.00 0.97 15
Brie Larson 1.00 1.00 1.00 15
Brit Marling 0.86 1.00 0.92 12
Bryan Cranston 0.88 1.00 0.93 14
Caity Lotz 1.00 1.00 1.00 12
Cameron Monaghan 1.00 1.00 1.00 14
Chadwick Boseman Face 0.89 1.00 0.94 17
Chance Perdomo 0.90 1.00 0.95 9
Chris Evans 1.00 0.93 0.96 14
Chris Pratt 1.00 1.00 1.00 14
Cobie Smulders 1.00 0.87 0.93 15
Danielle Panabaker 1.00 1.00 1.00 14
Dave Franco 1.00 1.00 1.00 13
David Mazouz 0.92 1.00 0.96 11
Dominic Purcell 0.92 1.00 0.96 12
Drake 1.00 0.86 0.92 7
Dua Lipa Face 1.00 1.00 1.00 11
Dwayne Johnson 1.00 0.92 0.96 12
Eliza Taylor 1.00 1.00 1.00 13
Elizabeth Olsen Face 1.00 1.00 1.00 10
Elon Musk 1.00 1.00 1.00 17
Emilia Clarke 1.00 0.85 0.92 13
Emily Bett Rickards 1.00 0.80 0.89 5
Emma Stone 1.00 1.00 1.00 14
Emma Watson Face 1.00 1.00 1.00 8
Gal Gadot Face 1.00 0.79 0.88 14
Grant Gustin Face 1.00 1.00 1.00 11
Gwyneth Paltrow 1.00 1.00 1.00 13
Henry Cavil 1.00 1.00 1.00 8
Jason Isaacs 1.00 0.93 0.96 14
Jason Momoa 0.53 0.89 0.67 9
Jeff Bezos 1.00 1.00 1.00 10
Jeremy Renner 1.00 1.00 1.00 8
Jesse Eisenberg 1.00 0.89 0.94 9
Jim Parsons 1.00 1.00 1.00 10
Jon Bernthal 0.92 1.00 0.96 11
Josh Radnor 1.00 1.00 1.00 11
Kiernan Shipka 1.00 0.93 0.96 14
Kit Harington 1.00 0.94 0.97 16
Kristen Stewart Face 0.92 1.00 0.96 12
Krysten Ritter 0.93 1.00 0.96 13
Kumail Nanjiani 0.92 1.00 0.96 11
Lindsey Morgan Face 1.00 1.00 1.00 10
Maisie Williams 1.00 0.89 0.94 9
Margot Robbie Face 0.86 1.00 0.92 12
Maria Pedraza 1.00 0.89 0.94 9
Mark Ruffalo 1.00 1.00 1.00 12
Mark Zuckerberg 0.94 0.94 0.94 17
Martin Starr 1.00 0.93 0.97 15
Melissa Benoit 1.00 0.89 0.94 18
Miguel Herran 1.00 1.00 1.00 16
Mike Colter 1.00 0.94 0.97 17
Millie Bobby Brown 1.00 1.00 1.00 17
Morena Baccarin 0.94 0.94 0.94 16
Morgan Freeman 0.80 0.80 0.80 10
Natalie Portman 1.00 1.00 1.00 10
Neil Patrick Harris 1.00 1.00 1.00 14
Paul Rudd 1.00 1.00 1.00 13
Pedro Alonso 1.00 1.00 1.00 12
Peter Dinklage 1.00 0.75 0.86 4
Rami Melek 1.00 1.00 1.00 15
Rihanna 0.89 0.67 0.76 12
Rj Mitte 1.00 1.00 1.00 20
Robert Downey Jr Face 1.00 1.00 1.00 9
Robert Knepper 1.00 0.94 0.97 18
Robin Taylor 0.85 0.94 0.89 18
Ryan Reynolds 1.00 1.00 1.00 13
Sarah Wayne Callies 0.93 1.00 0.97 14
Scarlett Johansson 1.00 0.90 0.95 10
Sean Pertwee 1.00 1.00 1.00 18
Sebastian Stan 0.92 0.92 0.92 13
Selena Gomez 0.89 1.00 0.94 8
Shakira 1.00 1.00 1.00 15
Sophie Turner 1.00 0.88 0.93 8
Stephen Amell 1.00 1.00 1.00 7
Sundar Pichai 1.00 1.00 1.00 9
Tati Gabrielle 1.00 1.00 1.00 9
Taylor Swift 1.00 1.00 1.00 13
Thomas Middleditch 1.00 1.00 1.00 12
Tom Cavanagh 0.89 0.89 0.89 9
Tom Holland Face 1.00 1.00 1.00 10
Ursula Corbero 1.00 1.00 1.00 6
Wentworth Miller 1.00 1.00 1.00 7
Willa Holland 0.79 1.00 0.88 11
William Fichtner 1.00 0.92 0.96 13
Zendaya 1.00 1.00 1.00 12
accuracy 0.97 1197
macro avg 0.97 0.96 0.96 1197
weighted avg 0.97 0.97 0.97 1197
test_image1 = load_image("C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/Benedict+Cumberbatch9.jpg")
test_image1 = (test_image1/255.).astype(np.float32)
test_image1 = cv2.resize(test_image1, dsize = (224, 224))
embedding = vgg_face_descriptor.predict(np.expand_dims(test_image1, axis = 0))[0]
embedding_scaled = sc.transform(embedding.reshape(1, -1))
test1_pred = svc_pca.predict(pca.transform(embedding_scaled))
test1_pred_encoded = en.inverse_transform(test1_pred)
1/1 [==============================] - 1s 510ms/step
print(test1_pred_encoded)
plt.imshow(test_image1)
['pins_Benedict Cumberbatch']
<matplotlib.image.AxesImage at 0x17b191c2c90>
test_image2 = load_image("C:/Users/ruhee/OneDrive/Desktop/AIML/Computer Vision/Dwayne+Johnson4.jpg")
test_image2 = (test_image2/255.).astype(np.float32)
test_image2 = cv2.resize(test_image2, dsize = (224, 224))
embedding = vgg_face_descriptor.predict(np.expand_dims(test_image2, axis = 0))[0]
embedding_scaled = sc.transform(embedding.reshape(1, -1))
test2_pred = svc_pca.predict(pca.transform(embedding_scaled))
test2_pred_encoded = en.inverse_transform(test2_pred)
1/1 [==============================] - 0s 492ms/step
print(test2_pred_encoded)
plt.imshow(test_image2)
['pins_Dwayne Johnson']
<matplotlib.image.AxesImage at 0x17b12df0650>
